The broad goal of this project is to adapt genomic mismatch scanning (GMS) to human disease gene mapping. This efficient gene mapping method seeks to identify large regions (5-20 kb) of sequence identity between two genomes on the basis that they likely represent regions of identity-by-descent (given the substantial rate of natural polymorphism in human populations). Identical-by-descent DNA is enriched in two steps: (i) after reannealing of the two genomes, heterohybrids are purified by using a combination of a restriction methylase and methylation-sensitive endonucleases, (ii) heterohybrids that contain mismatches are nicked in vitro by the E. coli MutHLS mismatch repair system and subsequently eliminated from the pool, leaving only mismatch-free heterohybrids. The genomic origin of this selected pool of DNA fragments is then mapped in a single hybridization step onto metaphase chromosomes or cloned ordered arrays In principle, the mapping power provided by GMS approaches the theoretical limit offered by an arbitrarily dense set of completely informative polymorphic markers, and results in a great increase in the effective number of informative markers without a corresponding increase in the number of individual tests. Currently, GMS has been validated in S. cerevisiae. The much larger size and substantial amount of repetitive DNA in the human genome pose technical hurdles. Size selection of restriction-digested human genomic DNA to yield simplified representations may facilitate the implementation of GMS to human disease gene mapping. On these simplified genomic pools of DNA, genomic solution hybridization and heteroduplex selection will be optimized. Individual restriction fragments will be monitored by Southern analysis through UMS selection to assess the relative enrichment of identical-by-descent fragments in the human genome. Mismatch dependent nicking will be optimized using either purified MutHLS proteins or crude protein extracts from E. coli. Hybridization of the GMS-selected DNA to metapahse chromosomes or ordered YAC arrays will be optimized. In addition, an extension of GMS to identify regions of homozygosity in a diploid genome is proposed to scan for regions of homozygosity-by-descent to map rare recessive traits and regions of loss-of-heterozygosity to map tumor suppressor genes. The rapid determination of genotypic concordance among affected relative pairs using GMS would allow the mapping of genetically complex traits. Many common human diseases including cancer, mental illnesses, diabetes, epilepsy, atherosclerosis, obesity, inflammatory bowel disease, hypertension, and asthma exhibit complex inheritance patterns. The determination of the genetic predisposition of these common diseases would have a major effect on medicine impacting on the diagnosis, prognosis, treatment, and prevention of these prevalent illnesses.